(1) Search and Assignment. As analyzed above, it can be seen that the IoU metric are suboptimal primarily because it is difficult to answer "which to match" question for each proposal. The performance is summarized in Table REF . When evaluated respectively, the search and assignment strategy shows the improvement of 4.0% MR\(^{-2}\)  compared with the original IoU assignment. Furthermore, by incorporating with the refinement module, we show a consistent improvement of 1.6% MR\(^{-2}\) . In addition, we can also see that from the visualization results in Figure REF , our algorithm effectively reduces false detection results when there are large height variance of pedestrians in each image compared with other algorithms, e.g., CSP [1]} or OR-CNN [2]}. Taking the first image in Figure REF  as an example, the red dotted box failed to regress toward the green box (boy with white T-shirt), and thus brings about "close but not correct" false positives. Moreover, our algorithm is particularly effective in two challenging scenarios. One is the scene with large height variance, such as the example in Figure REF  (first row, third column). The confusion in IoU makes it harder to train a high quality regressor, and thus brings about "close but not correct" false positives during testing, while ours can effectively eliminate this kind of low-quality regression. The second is the occlusion scene. In this case, our algorithm has more consistent matching targets and therefore has better performance (Figure REF , second row, second column).
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